Work

Fully automated esophagus segmentation with a hierarchical deep learning approach

Year: 2017

Type: conference-paper

Abstract: Segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper we focus on esophagus segmentation, a challenging problem since the walls of the esophagus have a very low contrast in CT images. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Experiments demonstrate competitive performance on a dataset of 30 CT scans.

Source: IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

DOI: 10.1109/icsipa.2017.8120664

Authors: Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan

Institutions: University of North Carolina at Chapel Hill, Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes, Université de Rouen Normandie, Institut National des Sciences Appliquées Rouen Normandie

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: European Commission, European Regional Development Fund, National Cancer Institute